Wildfires constitute an important threat to human lives and livelihoods worldwide, as well as a major ecological disturbance. However, available wildfire databases often provide incomplete or inaccurate information, namely regarding the timing and extension of fire events. In this study, we described a generic framework to compare, rank and combine multiple remotely-sensed indicators of wildfire disturbances, in order to not only select the best indicators for each specific case, as well as to provide multi-indicator consensus approaches that can be used to detect wildfire disturbances in space and time. [open to see the full abstract]

Geographic Object-based Image Analysis (GEOBIA) is increasingly used to process high-spatial resolution imagery, with applications ranging from single species detection to habitat and land cover mapping. Image segmentation plays a key role in GEOBIA workflows, allowing to partition images into homogenous and mutually exclusive regions. Nonetheless, segmentation techniques require a robust parameterization to achieve the best results. Frequently, inappropriate parameterization leads to sub-optimal results and difficulties in comparing distinct methods. Here, we present an approach based on Genetic Algorithms (GA) to optimize image segmentation parameters by using the performance scores from object-based classification, thus allowing to assess the adequacy of a segmented image in relation to the classification problem. This approach was implemented in a new R package called SegOptim, in which several segmentation algorithms are interfaced, mostly from open-source software (GRASS GIS, Orfeo Toolbox, RSGISLib, SAGA GIS, TerraLib), but also from proprietary software (ESRI ArcGIS). SegOptim also provides access to several machine-learning classification algorithms currently available in R, including Gradient Boosted Modelling, Support Vector Machines, and Random Forest. [open to see the full abstract]

Here we describe how satellite-derived Ecosystem Functional Attributes (EFAs), which offer a more integrative and faster evaluation of ecosystem responses to environmental change, can be used as predictors in Species Distribution Models (SDMs) and for implementing multi-scale species monitoring programs. [open to see the full abstract]

In this paper we describe a framework to evaluate post-fire recovery based on remotely-sensed measures of relative vegetation recovery, calculated from satellite NDVI time-series. Three indicators are proposed: the novel Cumulative Relative Recovery Index (CRRI), the Recovery Trend Index (RTI), and the Half Recovery Time index (HRT). Based on the proposed indicators, we investigated main factors driving post-fire dynamics with a set of Random Forest models. [open to see the full abstract]

We focus on Mediterranean wetlands in central Iberia and perform a multi-level, comparative study of two endemic pond-breeding amphibians, a salamander (Pleurodeles waltl) and a toad (Pelobates cultripes) and identified major factors associated with population connectivity through the analysis of three sets of variables potentially affecting gene flow at increasingly finer levels of spatial resolution. Overall, results suggest a positive role of structural heterogeneity in population connectivity in pond-breeding amphibians, with habitat patches of Mediterranean scrubland and open oak woodlands (“dehesas”) facilitating gene flow. [open to see the full abstract]

In this work we assessed the vulnerability of herptile species to future climate and land use changes in fragmented landscapes. We developed and tested a methodological approach combining the strengths of Species Distribution Models (SDMs) and of functional connectivity analysis. [open to see the full abstract]

In this paper we explored how satellite-based environmental variables can be fed into species distribution models (SDMs) to investigate species-environment relations and forecast responses to change. We address the spatiotemporal dynamics of species’ habitat suitability at the landscape level by combining multi-temporal RS data with SDMs for analysing inter-annual habitat suitability dynamics. [open to see the full abstract]

In this work we evaluated how very high-resolution colour imagery and digital surface models from an unmanned aerial vehicle can be effectively used for assessing habitat extent and condition in fine-scale disturbance-dependent mountain mosaics. [open to see the full abstract]